There is much that can be learned or inferred about an individual based on that person's collection of images, including hobbies and frequent activities, travel and vacation spots, pets, family, friends, and other interests. This type of information can be of particular interest to advertisers or to anyone soliciting funds or support. By learning about a person through their digital image records, an advertiser can more closely target sales, marketing, and promotional approaches to reach an interested audience.
Although it is recognized that much can be learned about a person's subject interests from their collection of digital image records, conventional techniques for obtaining this information remain fairly simplistic and have significant shortcomings. Techniques exist for obtaining semantic information from image data content for one or more images. For example, there are techniques, familiar to those skilled in the image analysis arts, for readily detecting people, animals, and various types of objects in a digital image. However, there is more to learning about a person's subject interests than simply decomposing image content into mere data units or labels for objects in the image and mechanically associating those objects with the user. Subject interests are more accurately learned from the images a person captures at various times and have at least some probabilistic relation to factors such as when and where pictures are captured, how often a particular person, place, event, or object recurs in the image collection, which people or objects tend to appear within the same images or in images taken within the same chronological event, and so on. A more accurate evaluation of user subject interests can help advertisers and others to more effectively relate their message, appeal, service, or product offering to an individual user.
There is, then, a need for a more systematic and robust approach for obtaining information about user subject interests from a user's collection of digital image records.